The Impact of Environmental Variability on Fishers’ Harvest Decisions in Chile

.title[
# The Impact of Environmental Variability on Fishers’ Harvest Decisions in Chile
]
.subtitle[
## Using a Multi-Species Approach
]
.author[
### Felipe J. Quezada-Escalona
]
.date[
### Encuentro EfD Chile 2025
]

---

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# Introduction

## Big picture

- Marine resource distribution and abundance is changing due to climate variability, with heterogenous spatial effects <a name=cite-Poloczanska2013-qq></a>([Poloczanska, Brown, Sydeman et al., 2013](#bib-Poloczanska2013-qq)).

- Harvest levels would be affected <a name=cite-Quezada2023-hc></a>([Quezada, Tommasi, Frawley et al., 2023](#bib-Quezada2023-hc)), as well as price and value of catches, fishing costs, fishers’ incomes, among others <a name=cite-sumaila2011></a>([Sumaila, Cheung, Lam et al., 2011](#bib-sumaila2011))

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Recordar que uno apreta C para clonar, P para Presenter View, H para tener un mapa de las teclas

---

# Introduction

## Research question

How will fishing decisions, aggregate catch levels, and the price of marine resources be affected under different climatic scenarios in the multispecies small pelagic fishery (SPF) in Chile?

- How do fishers **substitute between species**?
  - Contribute to the limited local literature on multi-species economic modeling in Chile
    - Understand fishers’ adaptive capacity helps to inform climate-resilient fisheries policies in Chile
    - See <a name=cite-Pena-Torres2017-gn></a>[Peña-Torres, Dresdner, and Vasquez (2017)](#bib-Pena-Torres2017-gn) for ENSO effects in Jack Mackerel fishery using discrete choice models.
  - We will focus in **climate variability** to estimate short-run responses.
    - i.e., climate change effect without adaptation <a name=cite-auffhammer2018></a>([Auffhammer, 2018](#bib-auffhammer2018))
    
---

# Introduction

## Why a Multi-Species Model?

- Diversification is a good strategy:
  - Improves income stability and climate resilience <a name=cite-Kasperski2013-jz></a><a name=cite-Finkbeiner2015-bs></a>([Kasperski and Holland, 2013](#bib-Kasperski2013-jz); [Finkbeiner, 2015](#bib-Finkbeiner2015-bs))
- Fishers respond to environmental variability by:
  - Maintaining the current strategy
  - **Reallocating effort to other species/areas <a name=cite-Gonzalez-Mon2021-kj></a>([Gonzalez-Mon, Bodin, Lindkvist et al., 2021](#bib-Gonzalez-Mon2021-kj))**
  - Exiting the fishery <a name=cite-Powell2022-wj></a>([Powell, Levine, and Ordonez-Gauger, 2022](#bib-Powell2022-wj))
- Under multispecies harvesting is not straighforward to study fisher harvest decisions
  - Responses to availability vary by (i) port infrastructure, (ii) market access, and (iii) regulations ([Powell, Levine, and Ordonez-Gauger, 2022](#bib-Powell2022-wj))
  - Different fishers, different choices <a name=cite-Jardine2020-um></a><a name=cite-Zhang2011-wv></a>([Jardine, Fisher, Moore et al., 2020](#bib-Jardine2020-um); [Zhang and Smith, 2011](#bib-Zhang2011-wv))

---

# Chile’s Small Pelagic Fishery

---

# Chile’s Small Pelagic Fishery (SPF)

## Some facts

- Mainly composed by anchoveta, Jack mackerel, Sardine
- ~94% of national catch <a name=cite-SUBPESCA2020></a>([SUBPESCA, 2020](https://www.subpesca.cl/portal/618/articles-106845_documento.pdf))
- Mostly harvested with purse-seiners
- In the Central-South (CS) region (Valparaiso-Los Lagos) all three species play a major role.
- SPF have been used primarily for fishmeal and fish oil production [@Pena-Torres2017-gn] (~85% of jack mackerel for reduction)
  
## Regulations
- Quota (TAC); divided between the small-scale and indutrial sector
- Industrial sector operates under ITQ
- RAE (*Regimen Artesanal de Extracción*) in some areas for small-scale fishery
  - Allocates regional quota to area or fishermen organization (i.e., catch shares)
- Anchoveta and sardine are regulated as a mixed-species fishery
  
---

# Chile’s Small Pelagic Fishery (SPF)

## Status of the stocks (CS)

- Anchoveta: 
  - Collapsed until 2018, 
  - Overexploited in 2019, and has 
  - Since 2020, within MSY limits.

- Sardine:
  - Within MSY levels, except in 2021 and 2023 (overexploited)

- Jack mackerel:
  - Overexploited until 2018, then within MSY limits. 
    
---

# Methods and data

---

# Methodology Overview

Based on <a name=cite-Kasperski2015-jm></a>[Kasperski (2015)](#bib-Kasperski2015-jm):

1. Econometrics models
  - Estimate stock dynamics
  - Estimate trip-level costs
  - Estimate annual trips
  - Estimate inverse demand

2. Simulations 
  - Obtain optimal harvest and quota levels
  - Simulate climate change effects on profits, harvest and prices.

---

# Data Sources

- From IFOP:
  - Trip-level data
    - ID, departure and arrival times, vessel capacity, fleet and gear type, ports of departure and landing, haul timing and location, species, retained catch. 
  - Annual stock abundance
  - Monthly landings by port.  
  - Prices paid by processing plants (IFOP surveys; month-region)

---

# Data Sources

## Environmental covariates

From E.U. Copernicus Marine Service Information:

- Global Ocean Physics Reanalysis <a name=cite-GLORYS12V1></a>([E.U. Copernicus Marine Service Information, 2025a](https://doi.org/10.48670/moi-00021))
  - Daily salinity, sea surface temperature, and current speed and direction
- Global Ocean Hourly Reprocessed Sea Surface Wind and Stress from Scatterometer and Model dataset <a name=cite-WIND_GLO_PHY></a>([E.U. Copernicus Marine Service Information, 2025b](https://doi.org/10.48670/moi-00185))
  - Hourly wind speed and direction at the surface
- Global Ocean Colour dataset <a name=cite-GlobColour></a>([E.U. Copernicus Marine Service Information, 2025c](https://doi.org/10.48670/moi-00281))
  - Chlorophyll-a concentration

**Some information:**
- 2012–2024
- Chilean EEZ (32°S to 41°S)
- Coarsest resolution: ~10 km

---

# Data Sources

## Environmental covariates

![](data:image/png;base64,#figs/env_data_map.svg)

---

# Data Sources

## To be requested

- Average wage pay to crew member per hour (available?)
- Diesel cost.
- Permits by vessels
- Quota prices (auction or secondary markets, if available)
  - Captures forward-looking behavior and information <a name=cite-Birkenbach2024></a>([Birkenbach, Lee, and Smith, 2024](https://doi.org/10.1086/727356)).
  - Simplify the dynamic model to a static one <a name=cite-reimer2022structural></a>([Reimer, Abbott, and Haynie, 2022](#bib-reimer2022structural)).
- Quota by area/fishing organization for small-scale sector, and ITQ for industrial (by vessel?)
  - Information about Reallocations of quotas

---

# Data Sources

## Data for projections

Bio-ORACLE:
  - Only decadal (e.g., 2040–2050) projections
  - Different climate scenarios
  - SST, salinity, currents and chlorophyll (4km resolution)
  - No winds; CMIP6 for winds? (~100 km).
  
  
---

# Data Sources

## Data for projections

![](data:image/png;base64,#figs/oracle.png){width=10%}

---

# Econometrics models

---

# Model 1: Stock Dynamics

`$$x_{i,y+1} + h_{iy} = \underbrace{(1 + r_i)x_{iy} + \eta_i x_{iy}^2}_{R_i(x_{iy})} + \underbrace{\sum_{j \neq i}^{n-1} a_{ij} x_{iy} x_{jy}}_{I_i(x_y)} + \rho_i Env_{iy} + \varepsilon_{iy} \quad i=1,\ldots,n$$`
- where: 
  - `$x_{iy}$` is the fish stock by species `$i=1,\ldots,n$` in year `$y$`, `$n$` is the total number of species, 
  - `$h_{iy}$` is the annual harvest of species `$i$` on year `$y$`, 
  - `$r_i$` is the intrinsic growth rate of the resource `$i$`,
  - `$\eta_i$` is a density-dependent factor related to the carrying capacity, 
  - `$\alpha_{ij}$` are the interaction parameters between species. 
  - `$Env_{iy}$` includes environmental covariates (SST and chlorophyll)

The system of `$n$` growth equations can be estimated simultaneously using SUR

---

# Model 2: Trip-Level Costs

`$$C_{vg} = \sum_{i=1}^{2n+M+k} \alpha_{g, \mathbf{X}_i} \mathbf{X}_{ivg} + \frac{1}{2} \sum_{i=1}^{2n+M+k} \sum_{j=1}^{2n+M+k} \alpha_{g, \mathbf{X}_i\mathbf{X}_j} \mathbf{X}_{ivg} \mathbf{X}_{jvg}$$`
where: 
- `$C_{vg}= w z_{vg}^*$` is the total cost incurred by vessel `$v=1,\ldots,V_g$` conditional on gear used `$g=1,\ldots,G$`:
  - `$z_{vg}^*$` is the optimal quantity of input used, (e.g., crew members, time spent at sea, distance traveled?)
  - `$w$` is a matrix of variable input prices, 
- `$\mathbf{X}^{'}_{vg} = [w;h_{vg};x;Z_v;Env]$` is a matrix of explanatory variables, and `$\mathbf{X}_{ivg}$` represents the *i*th column of the `$\mathbf{X}_{vg}$`:
  - `$h_{vg}$` is a matrix of harvest quantities, 
  - `$x$` is a matrix of given stock levels of the species of interest, and 
  - `$Z_v$` is a matrix of given vessel characteristics. 
  - `$Env$` is a matrix of **environmental covariates**
    - e.g., wind intensity and wave conditions in each trip at the harvest location? (or within a port radius?)

---

# Model 3: Total Annual Trips

The number of trips a vessel will take in a given year for each gear type used is assumed to follow a Poison distribution: 
`$$Pr\left[T^{*}_{vgy} = t_v\right] = \frac{exp^{-exp(U^{'}_{vg}\beta_g)}exp(U^{'}_{vg}\beta_g)^{t_v}}{t_v !}$$`
where 
- `$U_{vg}=[p,w,h_{vg},\bar{q},Z_{vg}, Env]$` is a matrix of explanatory variables,
  - `$p$` is a matrix of species prices, 
  - `$w$` is a matrix of variable input prices, 
  - `$h_{vg}$` is a matrix of harvest quantities, 
  - `$\bar{q}$` is the annual quota level.
  - `$Z_v$` is a matrix of given vessel characteristics. 
  - `$Env$` include variables that reflect annual weather conditions
    - Accumulation of *bad weather days*? or *number of storms*?
  - Other variables? State dependency?
- `$\beta_g$` is a vector of coefficients to be estimated, 
- `$t_v$` is the number of trips taken by vessel `$v$` using gear type `$g$` in year `$y$`, and

---

# Model 4: Inverse Demand

The price of each species is modeled using an Inverse Almost Ideal Demand System (IADS). The log of the price `$p_{iy}$` of a species `$i$` in year `$y$` is the following:

`$$\ln p_{iy} = \sum_j^n \gamma_j \ln h_{j,y} + \gamma_{H} \ln H_y + \gamma_{FM} \ln P^\text{FishMeal}_y + \epsilon_{iy}$$`
where:
- `$H_y = \sum h_{j,y}$`
- `$P^\text{FishMeal}_y$` is the fish meal world price

Harvest may be endogenous 
  - Three Stage Least Squares (3SLS) procedure, 
  - `$h_{j,y}$` instrumented by variables that affect supply function such as *SST*, *Chl*, and fuel prices.

---

# Numerical optimization

---

# Integration and Simulation

Use models parameters to:

- Obtain the optimal **harvest** and **quota** using historical data
- Conduct numerical optimization to obtain optimal **harvest** and **quota** conditional on climate scenario.
- Evaluate **profits** and **species substitution**

---

# Numerical optimization

## Vessel maximization problem

In each year, a vessel maximizes profits by choosing their optimal number of
trips `$T_g$` and harvest levels per trip `$h_{g\tau}$` given a gear type:
`$$\begin{align*}
\max_{h_{gt}, T_g} \quad \pi_{vgt} & = \sum_{\tau=t}^{T_g} \rho^\tau \left\{ P(h) h_{g\tau} - C_g(h_{g\tau} | w, x, Z, Env) \right\} \quad \tau = t,\ldots, T_g \nonumber \\
\textbf{s.t} \quad q_{g,t+1} & = \omega \ast \bar{q} - \sum_{t=1}^{t} h_{gt} \geq 0, \quad t = 1, \dots, T-1, \quad g = 1, \dots, G
\end{align*}$$`

- where:
  - `$\rho$` is the intra-annual discount factor, 
  - `$\omega$` is a vector of shares of `$\bar{q}$`, and 
  - `$h_{lt}=0$` for all `$l\neq g$`.

---

# Numerical optimization

## Some considerations

- The vector of shares is obtained from historical data on harvest. 
- The optimal profit from the maximization problem is `$\pi_{vgy}^* (p,w,x,Z,\bar{q},\omega, Env)$`, 
  - `$h_{vgty}^*$` is the optimal harvest per trip.
  - `$T_{vgy}^*$` optimal total number of trips.
- Optimal quota level, per year and by species, is obtained by solving the social-planner optimization problem to maximize the net value
of the fishery

---

# Preliminary results

---

# Is there any subtitution?

## Small-scale vessels

---

# Is there any subtitution?

## Industrial vessels

---

# Stock dynamics

---

# Stock dynamics

- SST and CHL improves model performance (F = 1.908; `$p$`-value = 0.07).
  - Test if I can exclude interactions terms
- Herrick, et al. (2009): "Sardine is known to be more productive during warm-water regimes in the California Current ecosystem."
- Need to calculate robust standard error (by hand?)

---

# What is next?

- Finish biomass estimations
- Start soon with total annual trips 
  - Hopefully a short paper can came up from that work
- Two undergrad students working on the inverse demand (i.e. price) module for their thesis... 
  - Results by July 2026
  - If they have time, they will also analyze long-run and short-run dynamics with a VEC
  - Write paper from their dissertation

---

# ¡Muchas gracias!

<span style="color:#f59f18; font-size:1.3em; font-weight:bold;">¿Preguntas?</span>

**Felipe J. Quezada-Escalona**

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🌐 felipequezada.com
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---

# References

<a name=bib-auffhammer2018></a>[Auffhammer, M.](#cite-auffhammer2018)
(2018). "Quantifying economic damages from climate change". In:
_Journal of Economic Perspectives_ 32.4, pp. 33-52.

<a name=bib-Birkenbach2024></a>[Birkenbach, A. M., M. Lee, and M. D.
Smith](#cite-Birkenbach2024) (2024). "Counterfactual Modeling of
Multispecies Fisheries Outcomes under Market-Based Regulation". In:
_Journal of the Association of Environmental and Resource Economists_
11.3, pp. 755-796. DOI:
[10.1086/727356](https://doi.org/10.1086%2F727356). eprint:
https://doi.org/10.1086/727356.

<a name=bib-GlobColour></a>[E.U. Copernicus Marine Service
Information](#cite-GlobColour) (2025c). _Global Ocean Colour
(Copernicus-GlobColour)_. Accessed on 23-SEP-2025. DOI:
[10.48670/moi-00281](https://doi.org/10.48670%2Fmoi-00281).

<a name=bib-WIND_GLO_PHY></a>[E.U. Copernicus Marine Service
Information](#cite-WIND_GLO_PHY) (2025b). _Global Ocean Hourly
Reprocessed Sea Surface Wind and Stress from Scatterometer and Model_.
Accessed on 23-SEP-2025. DOI:
[10.48670/moi-00185](https://doi.org/10.48670%2Fmoi-00185).

<a name=bib-GLORYS12V1></a>[E.U. Copernicus Marine Service
Information](#cite-GLORYS12V1) (2025a). _Global Ocean Physics
Reanalysis_. Accessed on 23-SEP-2025. DOI:
[10.48670/moi-00021](https://doi.org/10.48670%2Fmoi-00021).

---

# References

<a name=bib-Finkbeiner2015-bs></a>[Finkbeiner, E.
M.](#cite-Finkbeiner2015-bs) (2015). "The role of diversification in
dynamic small-scale fisheries: Lessons from Baja California Sur,
Mexico". In: _Glob. Environ. Change_ 32, pp. 139-152.

<a name=bib-Gonzalez-Mon2021-kj></a>[Gonzalez-Mon, B., Ö. Bodin, E.
Lindkvist, et al.](#cite-Gonzalez-Mon2021-kj) (2021). "Spatial
diversification as a mechanism to adapt to environmental changes in
small-scale fisheries". In: _Environ. Sci. Policy_ 116, pp. 246-257.

<a name=bib-Jardine2020-um></a>[Jardine, S. L., M. C. Fisher, S. K.
Moore, et al.](#cite-Jardine2020-um) (2020). "Inequality in the
Economic Impacts from Climate Shocks in Fisheries: The Case of Harmful
Algal Blooms". In: _Ecol. Econ._ 176, p. 106691.

<a name=bib-Kasperski2015-jm></a>[Kasperski,
S.](#cite-Kasperski2015-jm) (2015). "Optimal Multi-species Harvesting
in Ecologically and Economically Interdependent Fisheries". In:
_Environ. Resour. Econ._ 61.4, pp. 517-557.

<a name=bib-Kasperski2013-jz></a>[Kasperski, S. and D. S.
Holland](#cite-Kasperski2013-jz) (2013). "Income diversification and
risk for fishermen". En. In: _Proc. Natl. Acad. Sci. U. S. A._ 110.6,
pp. 2076-2081.

---

# References

<a name=bib-Pena-Torres2017-gn></a>[Peña-Torres, J., J. Dresdner, and
F. Vasquez](#cite-Pena-Torres2017-gn) (2017). "El Niño and Fishing
Location Decisions: The Chilean Straddling Jack Mackerel Fishery". In:
_Mar. Resour. Econ._ 32.3, pp. 249-275.

<a name=bib-Poloczanska2013-qq></a>[Poloczanska, E. S., C. J. Brown, W.
J. Sydeman, et al.](#cite-Poloczanska2013-qq) (2013). "Global imprint
of climate change on marine life". En. In: _Nat. Clim. Chang._ 3.10,
pp. 919-925.

<a name=bib-Powell2022-wj></a>[Powell, F., A. Levine, and L.
Ordonez-Gauger](#cite-Powell2022-wj) (2022). "Climate adaptation in the
market squid fishery: fishermen responses to past variability
associated with El Niño Southern Oscillation cycles inform our
understanding of adaptive capacity in the face of future climate
change". En. In: _Clim. Change_ 173.1-2, p. 1.

<a name=bib-Quezada2023-hc></a>[Quezada, F. J., D. Tommasi, T. H.
Frawley, et al.](#cite-Quezada2023-hc) (2023). "Catch as catch can:
markets, availability, and fishery closures drive distinct responses
among the U.S. West Coast coastal pelagic species fleet segments". En.
In: _Can. J. Fish. Aquat. Sci._. Just-IN.

<a name=bib-reimer2022structural></a>[Reimer, M. N., J. K. Abbott, and
A. C. Haynie](#cite-reimer2022structural) (2022). "Structural
behavioral models for rights-based fisheries". In: _Resource and Energy
Economics_ 68, p. 101294.

---

# References

<a name=bib-SUBPESCA2020></a>[SUBPESCA](#cite-SUBPESCA2020) (2020).
_Informe Sectorial de Pesca y Acuicultura 2019_. Accessed: 02-04-2025.
URL:
[https://www.subpesca.cl/portal/618/articles-106845_documento.pdf](https://www.subpesca.cl/portal/618/articles-106845_documento.pdf).

<a name=bib-sumaila2011></a>[Sumaila, U. R., W. W. Cheung, V. W. Lam,
et al.](#cite-sumaila2011) (2011). "Climate change impacts on the
biophysics and economics of world fisheries". In: _Nature climate
change_ 1.9, pp. 449-456.

<a name=bib-Zhang2011-wv></a>[Zhang, J. and M. D.
Smith](#cite-Zhang2011-wv) (2011). "Heterogeneous Response to Marine
Reserve Formation: A Sorting Model approach". In: _Environ. Resour.
Econ._ 49.3, pp. 311-325.